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Martinez-Marin, M.

Publications and source records attributed to Martinez-Marin, M..

3 recordsLinked to original sources

Spiral brain dynamics track the temporal unfolding of subjective intensity during the N,N-dimethyltryptamine experience

Psychedelic compounds such as N,N-dimethyltryptamine (DMT) induce rapid and profound changes in conscious experience, requiring neuroimaging measures that capture how brain dynamics evolve across space and time. Emerging evidence suggests that rotational (vortex-like) dynamics may contribute to the coordination of large-scale neural activity. Here, we introduce two vorticity-based metrics; spiral vorticity (SV) and spatial spiral vorticity dispersion (SSVD), to quantify the rotational organization and spatial heterogeneity of large-scale cortical phase dynamics. Using fMRI data acquired under placebo and DMT in a within-subject design, we assessed these measures globally, across resting-state networks, and regionally to examine their relationship to subjective intensity. DMT increased SV and SSVD and reduced temporal irreversibility, indicating stronger rotational organization, greater spatial heterogeneity, and altered directional structure of cortical dynamics. Effects were non-uniform across functional systems, with the largest changes in default mode, visual, frontoparietal, and dorsal attention networks. Both vorticity measures tracked the temporal evolution of subjective intensity globally, while network-level analyses revealed spatially heterogeneous brain-experience coupling. Spatiotemporally resolved analyses further showed that the strongest reorganization of spiral dynamics emerged during offset and recovery rather than at peak subjective intensity. These findings identify spiral dynamics as a time-resolved neural signature of the DMT experience. By quantifying the rotational structure and spatial heterogeneity of cortical phase flow, vorticity-based measures provide a framework for linking large-scale brain dynamics to evolving subjective experience.

neuroscience↗

Modeling Hierarchical Brain Dynamics Outperforms Hormonal Biomarkers in Predicting Menstrual Cycle Phases

Hormonal fluctuations across the menstrual cycle influence large-scale brain dynamics, yet the underlying neurobiological mechanisms remain poorly understood. In this study, 60 nat-urally cycling women were scanned using resting-state fMRI during the early follicular, pre-ovulatory, and mid-luteal phases. We then applied a thermodynamics-inspired framework to explore the functional hierarchical organization of whole-brain dynamics across these phases. First, we found that brain dynamics are significantly modulated by estradiol, progesterone, and age across multiple resting-state networks. Second, to elucidate underlying mechanisms, we es-timated generative effective connectivity (GEC) matrices using whole-brain models and trained support vector machine classifiers to predict menstrual phases. These model-based biomarkers outperformed traditional functional connectivity and hormone measures in classifying men-strual cycle phases. These findings reveal that menstrual cycle-related changes modulate the hierarchical reorganization of brain dynamics, highlighting the potential of model-based ap-proaches to advance womens brain health research.

neuroscience↗

Disrupted hierarchical organization in disorders of consciousness revealed by fluctuation-dissipation deviations

Evaluating consciousness levels after coma remains clinically challenging, and probing the brains functional hierarchy offers model-based biomarkers of brain states. We characterize the hierarchy loss in disorders of consciousness (DoC) via departures from non-equilibrium dynamics. Irreversible, directed interactions are indexed by deviation from the fluctuation- dissipation theorem (FDT), computed from individualized whole-brain models fit to fMRI from controls and patients in minimally conscious state (MCS) or unresponsive wakefulness syndrome (UWS). Global and resting-state network dynamics in DoC were closer to equilibrium than in controls, decreasing stepwise with decreasing levels of consciousness. Mapping site-specific hierarchical drive over the system revealed disruptions within default-mode network components (e.g., medial and dorsolateral superior frontal gyrus) and subcortical hubs (e.g., thalamus, pallidum and putamen) differentiating between all groups. Recovery of near-control hierarchy in the visual network differentiated MCS from UWS, whereas multiple limbic areas showed similar abnormalities across both DoC groups. Together, these results identify non-equilibrium dynamics as a signature of conscious capacity and stablish FDT deviation as a principled, model-based hierarchy measure that can be operationalised for clinical stratification and monitoring, opening avenues for targeted in silico intervention planing.

neuroscience↗